The present invention relates to the use of musical principles in the sonification of data. More particularly, but not exclusively, the invention relates to a method and system to represent data with music utilizing generic fractal algorithm techniques. Currently, most data is represented visually in various two-dimensional and three-dimensional platforms. However, we live in a world filled with sound and receive a wide range of information aurally. As we drive our car we hear the tires on the road, the engine, the wind on the car, and other cars. By adding this information to our visual cueing, we more fully understand our environment. Sound directs our viewing and adds essential contextual information.
Numerous efforts have been made to sonify data; that is, represent data with sounds. However, rather than employing a musical approach, these efforts map data directly to various aspects of sound, resulting in a medium that is difficult to understand or irritating to listen to. The approach presented here is unique in that it uses musical principles to overcome these drawbacks. Moreover, unlike direct mapping from data to sound, which can only bring out the micro-scale aspects of the data, music can highlight the connection between the micro and macro scale. Additionally, because music can convey a large amount of information, it can enable users to perceive more facets of the data.
Currently, there are two main approaches to sonification of data. The primary difference between them is the means by which the sound is produced. One approach is directly mapping data parameters to various sound parameters (e.g., frequency, vibrato, reverberation) via synthesis algorithms. One of the largest efforts using this approach is the Scientific Sonification Project at the University of Illinois-Urbana/Champaign (Kaper and Tipei, 1998). A second approach utilizes MIDI parameters to represent data as pitch, volume, pre-made instrumental and vocal sounds, and rhythmic durations. This approach opens a broader range of sonification options but complicates the mapping of the data parameters to the sound parameters. Two sonification toolkits—Listen and MUSE (Musical Sonification Environment)—are the primary vehicles for this approach (Wilson and Lodha, 1996 and Lodha et al., 1997). In both approaches, the data is directly mapped with little effort to understand the underlying micro- and macro-scale patterns within the data and the relationship between them.
One way direct mapping of data to sound is accomplished is by assigning variable data to specific pitches or note values. FIG. 1 provides an example of direct mapping of data to specific pitches. The equivalent of direct mapping in the visual world would be assigning color to specific values and regions of three-dimensional space without further data transformation. This results in an incomprehensible conglomeration of color. However, if transformation of the data recognizes the underlying physics of the data, the data is instead comprehensible, and patterns and nuances in the data can be identified.
Therefore, despite advancements in the art, problems remain. Therefore, it is a primary object, feature, or advantage of the present invention to improve upon the state of the art.
It is another object, feature, or advantage of the present invention to apply a musical approach to the sonification of data.
It is a further object, feature, or advantage of the present invention to provide a method and system for creating data-driven music that does not rely upon directly mapping sounds to data.
A still further object, feature, or advantage of the present invention is to provide for sonification of data that is not annoying and is not difficult to understand.
A further object, feature, or advantage of the present invention is to provide for sonification of data that includes phrasing and a sense of forward movement in the sound.
A still further object, feature, or advantage of the present invention is to provide for sonification of data that reveals the rich complexity of the details of the data.
Another object, feature, or advantage of the present invention is to provide a method and system for creating data-driven music that builds in listenability and flexibility for broad applicability to different types of data without external intervention by a composer.
Yet another object, feature, or advantage of the present invention is to provide a method and system for creating data-driven music that incorporates an understanding of how musical phrasing, sentence completion, and listenability are achieved within music.
Yet another object, feature, or advantage of the present invention is to provide for the development of nontonal/atonal music tools to provide a much larger design space with a construction of listenable music.
A further object, feature, or advantage of the present invention is the use of fractal algorithms—specifically Lindenmayer-Systems (L-Systems) to map data into patterns and details that enable the listener to understand the data.
A still further object, feature, or advantage of the present invention is the development of a context sensitive grammar that can capture the interrelationships between parts of the data.
Another object, feature, or advantage of the present invention is to provide a connection between micro- and macro-scales of the data.
Yet another object, feature, or advantage of the present invention is to provide a method for sonification of data that can be used with diverse types of data sets.
One or more of these and/or other objects, features, or advantages of the present invention will become apparent from the specification and claims that follow.